Adaptation of an mHealth Solution for the Nutritional Management of Diabetes in a Low- and Middle-Income Country: Pre-Post Mixed Methods Pilot Study
Bibliographic record
Abstract
Background: Carbohydrate counting (CC) is vital for individuals living with type 1 diabetes mellitus (T1DM); yet, formal training is often lacking in many contexts. To bridge this gap, the parents of a person living with diabetes and a team at the Geneva University Hospital (HUG) developed WebDia, a free-access app that helps patients with T1DM assess meal carbohydrates and make informed decisions regarding insulin dosage. In the context of Peru, where dietary patterns and meal compositions may differ, customizing WebDia to suit the components of the Peruvian diet becomes particularly relevant. Objective: This study aimed to customize WebDia according to the composition of the Peruvian diet to facilitate CC, and to provide training to health care workers (HCWs), children and adolescents living with T1DM, and their caregivers in the proficient use of WebDia-Mundi (new version of the Swiss app WebDia adapted to other geographic contexts). Methods: A dietitian compiled a database of Peruvian foods and their carbohydrate content. This was reviewed by a Swiss nurse specialized in diabetes, a Peruvian pediatric endocrinologist, and 2 researchers. Validation was conducted with a small group of children and adolescents living with T1DM and their caregivers. Subsequently, a 3-day workshop was held in 3 Peruvian regions for HCW and children and adolescents living with T1DM. The first 2 days were a training course for HCW to gain knowledge in T1DM and learn CC skills. This was followed by a 1-day workshop involving HCW, children and adolescents living with T1DM, and their caregivers. At the end of the workshop and 3 months later, an evaluation was performed to assess the app's usability, glycated hemoglobin, quality of life, and knowledge perception for children and adolescents living with T1DM and their caregivers. Furthermore, changes in knowledge among HCWs and overall workshop satisfaction were measured. Results: WebDia-Mundi was customized for the Peruvian context in 2022-2023. The training was attended by 25 HCWs, 25 children and adolescents living with T1DM, and 31 caregivers. Following the training, HCWs exhibited a significant 3.5-point increase in their knowledge of T1DM, while achieving positive results regarding the usability of WebDia-Mundi. Children and adolescents living with T1DM and their caregivers also reported a favorable perception of the ease of use and functionality of WebDia-Mundi, which enhanced their CC skills. Conclusions: This study underscores the importance of collaboration among multidisciplinary teams and the involvement of individuals with T1DM. Adapting mobile health solutions to new contexts and sharing experiences can help standardize this process.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".